{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The second stage of training after `Train DeBERTa-v3-base on pseudo-labeled data`\n",
    "Finetuning on `bigcode/pii-for-code-v2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset, load_metric, Dataset, DatasetDict, load_from_disk\n",
    "from huggingface_hub import notebook_login\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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     "referenced_widgets": [
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      "40b4b89c55894cad9722d9c52a2af235",
      "ed568e897d1c4b7a9eb2c9e69997d29b",
      "aa0798372ed149a18994104d7f9a3d97",
      "3ab74c3f04634c16b24630f9b6d1798c"
     ]
    },
    "id": "e-wI3YzurfCD",
    "outputId": "5c012504-773c-44b2-9f4e-a55079ef4b26"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5028fd915b1243feb0c2350ec98f6cb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "nHXDl8ifroF8"
   },
   "outputs": [],
   "source": [
    "FOLD = 0\n",
    "\n",
    "model_checkpoint = \"microsoft/deberta-v3-base\"\n",
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "IreSlFmlIrIm"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/monty/projects/pii-ner/utils/misc.py:38: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  _seqeval_metric = load_metric(\"seqeval\")\n",
      "Using the latest cached version of the module from /home/monty/.cache/huggingface/modules/datasets_modules/metrics/seqeval/c8563af43bdce095d0f9e8b8b79c9c96d5ea5499b3bf66f90301c9cb82910f11 (last modified on Thu Feb 16 17:58:29 2023) since it couldn't be found locally at seqeval, or remotely on the Hugging Face Hub.\n",
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2ForTokenClassification: ['lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'deberta.embeddings.position_embeddings.weight', 'lm_predictions.lm_head.dense.bias', 'mask_predictions.dense.bias', 'lm_predictions.lm_head.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.LayerNorm.bias', 'mask_predictions.classifier.bias']\n",
      "- This IS expected if you are initializing DebertaV2ForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DebertaV2ForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DebertaV2ForTokenClassification were not initialized from the model checkpoint at microsoft/deberta-v3-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/transformers/convert_slow_tokenizer.py:434: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
      "  warnings.warn(\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer\n",
    "from transformers import DebertaV2TokenizerFast\n",
    "\n",
    "from utils import LABEL2ID, ID2LABEL\n",
    "\n",
    "\n",
    "model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=len(ID2LABEL))\n",
    "tokenizer = DebertaV2TokenizerFast.from_pretrained(model_checkpoint, add_prefix_space=True)\n",
    "\n",
    "model.config.id2label = {str(i):label for i, label in enumerate(ID2LABEL)}\n",
    "model.config.label2id = LABEL2ID\n",
    "\n",
    "tokenizer.model_max_length = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForTokenClassification.from_pretrained('deberta-v3-base-pretrained/checkpoint-3000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105,
     "referenced_widgets": [
      "40e9832b79644dfbbcd6cd6ea8bfc8c7",
      "9c872cf485ad4ae8b8359c33f7c3ea65",
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      "e0c423cb57e949f08255453100fd3e69",
      "3bf5b65c1c9a4ce0be50e3e2fce60c87"
     ]
    },
    "id": "s_AY1ATSIrIq",
    "outputId": "db669025-726d-4e81-e7eb-7133640aacfd"
   },
   "outputs": [],
   "source": [
    "dataset = dev_dataset = load_dataset(\"bigcode/pii-for-code-v2/\", use_auth_token=True)['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /data3/monty/datasets/pii-for-code-folds/train/cache-508fbacd9f7bdf6a.arrow\n",
      "Loading cached processed dataset at /data3/monty/datasets/pii-for-code-folds/train/cache-a8ea847edbdece61.arrow\n",
      "Loading cached processed dataset at /data3/monty/datasets/pii-for-code-folds/train/cache-3686d4caf026edda.arrow\n",
      "Loading cached processed dataset at /data3/monty/datasets/pii-for-code-folds/train/cache-1174fa42815e03f0.arrow\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['content', 'language', 'license', 'path', 'annotation_id', 'pii', 'id', 'fold', 'input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'labels'],\n",
       "        num_rows: 300\n",
       "    })\n",
       "    dev: Dataset({\n",
       "        features: ['content', 'language', 'license', 'path', 'annotation_id', 'pii', 'id', 'fold', 'input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'labels'],\n",
       "        num_rows: 100\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import label_tokenized, chunk_dataset\n",
    "\n",
    "def tokenize_and_label(entry, tokenizer=tokenizer):\n",
    "    inputs = tokenizer.encode_plus(entry['content'], return_offsets_mapping=True, add_special_tokens=False)\n",
    "    entry.update(inputs)\n",
    "    return label_tokenized(entry)\n",
    "\n",
    "dataset = dataset.map(lambda x: dict(pii=json.loads(x['pii'])))\n",
    "dataset = dataset.map(tokenize_and_label)\n",
    "\n",
    "dataset =  DatasetDict(\n",
    "    train = dataset.filter(lambda x: x['fold']!=FOLD),\n",
    "    dev = dataset.filter(lambda x: x['fold']==FOLD)\n",
    ")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdf2d756ade541c78d33b7df66876ea1",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e90878d30b9c48c3a852cc95025c51a9",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5945beeca284b94a0797c1e2b52b677",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels', 'id', 'chunk_id'],\n",
       "        num_rows: 1527\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels', 'id', 'chunk_id'],\n",
       "        num_rows: 513\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels', 'id', 'chunk_id'],\n",
       "        num_rows: 966\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ner_dataset = DatasetDict(\n",
    "    train = chunk_dataset(dataset['train'], tokenizer),\n",
    "    validation = chunk_dataset(dataset['dev'], tokenizer),\n",
    "    test = chunk_dataset(dataset['dev'], tokenizer, overlap_freq=2),\n",
    ")\n",
    "ner_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "from transformers import DataCollatorForTokenClassification, EarlyStoppingCallback\n",
    "\n",
    "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
    "model_name = model_checkpoint.split(\"/\")[-1]\n",
    "args = TrainingArguments(\n",
    "    f\"{model_name}-finetuned\",\n",
    "    overwrite_output_dir=True,\n",
    "    evaluation_strategy = \"steps\",\n",
    "    save_strategy='steps',\n",
    "    num_train_epochs=100,\n",
    "    eval_steps=300,\n",
    "    save_steps=300,\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    metric_for_best_model=\"f1\",\n",
    "    load_best_model_at_end = True,\n",
    "    weight_decay=0.01,\n",
    "    logging_steps=10,\n",
    "    save_total_limit=30,\n",
    "    push_to_hub=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "imY1oC3SIrJf"
   },
   "outputs": [],
   "source": [
    "from utils import compute_metrics\n",
    "\n",
    "trainer = Trainer(\n",
    "    model,\n",
    "    args,\n",
    "    train_dataset=ner_dataset[\"train\"],\n",
    "    eval_dataset=ner_dataset[\"validation\"],\n",
    "    data_collator=data_collator,\n",
    "    tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics,\n",
    "    callbacks=[EarlyStoppingCallback(early_stopping_patience = 30, early_stopping_threshold= 1e-3)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running training *****\n",
      "  Num examples = 1527\n",
      "  Num Epochs = 100\n",
      "  Instantaneous batch size per device = 16\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 9600\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='9600' max='9600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [9600/9600 2:11:07, Epoch 100/100]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Avg.precision</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Email</th>\n",
       "      <th>Ip Address</th>\n",
       "      <th>Key</th>\n",
       "      <th>Name</th>\n",
       "      <th>Password</th>\n",
       "      <th>Username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.010239</td>\n",
       "      <td>0.906294</td>\n",
       "      <td>0.550432</td>\n",
       "      <td>0.620130</td>\n",
       "      <td>0.583206</td>\n",
       "      <td>0.950495</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.793651</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.869234</td>\n",
       "      <td>0.561350</td>\n",
       "      <td>0.594156</td>\n",
       "      <td>0.577287</td>\n",
       "      <td>0.950495</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.816667</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.313167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.013248</td>\n",
       "      <td>0.872781</td>\n",
       "      <td>0.523121</td>\n",
       "      <td>0.587662</td>\n",
       "      <td>0.553517</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.776860</td>\n",
       "      <td>0.867925</td>\n",
       "      <td>0.290429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.013298</td>\n",
       "      <td>0.862504</td>\n",
       "      <td>0.728000</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.652330</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.765217</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.403670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.014682</td>\n",
       "      <td>0.863409</td>\n",
       "      <td>0.646853</td>\n",
       "      <td>0.600649</td>\n",
       "      <td>0.622896</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.803279</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1800</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.010992</td>\n",
       "      <td>0.891844</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.950495</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.816667</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.627692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.015342</td>\n",
       "      <td>0.854070</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.824561</td>\n",
       "      <td>0.679245</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>0.901961</td>\n",
       "      <td>0.313167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2400</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.019116</td>\n",
       "      <td>0.849698</td>\n",
       "      <td>0.609428</td>\n",
       "      <td>0.587662</td>\n",
       "      <td>0.598347</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.803419</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.320896</td>\n",
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       "    <tr>\n",
       "      <td>2700</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.021797</td>\n",
       "      <td>0.831837</td>\n",
       "      <td>0.669231</td>\n",
       "      <td>0.564935</td>\n",
       "      <td>0.612676</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.747826</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.352423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.019296</td>\n",
       "      <td>0.848694</td>\n",
       "      <td>0.565625</td>\n",
       "      <td>0.587662</td>\n",
       "      <td>0.576433</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.507042</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.789916</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.324528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3300</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.017736</td>\n",
       "      <td>0.867401</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.642336</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.722689</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.415842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3600</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.013048</td>\n",
       "      <td>0.915161</td>\n",
       "      <td>0.677536</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.640411</td>\n",
       "      <td>0.950495</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.389105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3900</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.014920</td>\n",
       "      <td>0.905559</td>\n",
       "      <td>0.688645</td>\n",
       "      <td>0.610390</td>\n",
       "      <td>0.647160</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.786325</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.384314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4200</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.015799</td>\n",
       "      <td>0.901762</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.603896</td>\n",
       "      <td>0.636986</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.765217</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.386100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.015760</td>\n",
       "      <td>0.903071</td>\n",
       "      <td>0.691228</td>\n",
       "      <td>0.639610</td>\n",
       "      <td>0.664418</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.803419</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.430189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4800</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.017878</td>\n",
       "      <td>0.894785</td>\n",
       "      <td>0.725806</td>\n",
       "      <td>0.584416</td>\n",
       "      <td>0.647482</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.379310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5100</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.021836</td>\n",
       "      <td>0.864189</td>\n",
       "      <td>0.742489</td>\n",
       "      <td>0.561688</td>\n",
       "      <td>0.639556</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.765217</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.342593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018239</td>\n",
       "      <td>0.894501</td>\n",
       "      <td>0.717131</td>\n",
       "      <td>0.584416</td>\n",
       "      <td>0.644007</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.377682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016453</td>\n",
       "      <td>0.896073</td>\n",
       "      <td>0.688645</td>\n",
       "      <td>0.610390</td>\n",
       "      <td>0.647160</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.775862</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.398438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015573</td>\n",
       "      <td>0.898120</td>\n",
       "      <td>0.777003</td>\n",
       "      <td>0.724026</td>\n",
       "      <td>0.749580</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.760331</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.643939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015133</td>\n",
       "      <td>0.903858</td>\n",
       "      <td>0.750877</td>\n",
       "      <td>0.694805</td>\n",
       "      <td>0.721754</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.778761</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.583026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014865</td>\n",
       "      <td>0.904744</td>\n",
       "      <td>0.831650</td>\n",
       "      <td>0.801948</td>\n",
       "      <td>0.816529</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.786325</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.791367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014461</td>\n",
       "      <td>0.908368</td>\n",
       "      <td>0.849315</td>\n",
       "      <td>0.805195</td>\n",
       "      <td>0.826667</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.910199</td>\n",
       "      <td>0.844068</td>\n",
       "      <td>0.808442</td>\n",
       "      <td>0.825871</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.779661</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.814545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.908146</td>\n",
       "      <td>0.822148</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.808581</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.786325</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.830325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015939</td>\n",
       "      <td>0.905687</td>\n",
       "      <td>0.766892</td>\n",
       "      <td>0.737013</td>\n",
       "      <td>0.751656</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>0.722022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016767</td>\n",
       "      <td>0.901454</td>\n",
       "      <td>0.734483</td>\n",
       "      <td>0.691558</td>\n",
       "      <td>0.712375</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.649446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>0.905149</td>\n",
       "      <td>0.807560</td>\n",
       "      <td>0.762987</td>\n",
       "      <td>0.784641</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.831858</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.792727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.899965</td>\n",
       "      <td>0.755172</td>\n",
       "      <td>0.711039</td>\n",
       "      <td>0.732441</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.807018</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.691176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016755</td>\n",
       "      <td>0.901299</td>\n",
       "      <td>0.780405</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.764901</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.738351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.017001</td>\n",
       "      <td>0.899909</td>\n",
       "      <td>0.759322</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.742952</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.717391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.900381</td>\n",
       "      <td>0.763333</td>\n",
       "      <td>0.743506</td>\n",
       "      <td>0.753289</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.735714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-300\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-300/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-300/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-300/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-300/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-600\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-600/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-600/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-600/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-600/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-900\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-900/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-900/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-900/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-900/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1200\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1200/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1200/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1200/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1200/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1500\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1500/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1500/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1500/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1800\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1800/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1800/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1800/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-1800/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2100\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2100/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2100/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2100/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2100/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2400\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2400/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2400/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2400/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2400/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2700\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2700/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2700/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2700/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-2700/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3000\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3000/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3000/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3000/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3000/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3300\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3300/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3300/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3300/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3300/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3600\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3600/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3600/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3600/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3600/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3900\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3900/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3900/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3900/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-3900/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4200\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4200/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4200/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4200/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4200/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4500\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4500/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4500/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4500/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4800\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4800/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4800/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4800/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-4800/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5100\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5100/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5100/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5100/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5100/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5400\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5400/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5400/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5400/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5400/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5700\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5700/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5700/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5700/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-5700/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6000\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6000/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6000/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6000/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6000/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6300\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6300/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6300/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6300/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6300/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6600\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6600/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6600/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6600/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6600/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6900\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6900/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6900/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6900/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6900/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7200\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7200/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7200/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7200/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7200/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7500\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7500/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7500/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7500/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7800\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7800/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7800/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7800/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-7800/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8100\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8100/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8100/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8100/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8100/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8400\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8400/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8400/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8400/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8400/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8700\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8700/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8700/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8700/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-8700/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9000\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9000/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9000/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9000/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9000/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9300\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9300/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9300/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9300/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9300/special_tokens_map.json\n",
      "Deleting older checkpoint [/data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-300] due to args.save_total_limit\n",
      "The following columns in the evaluation set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 513\n",
      "  Batch size = 16\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9600\n",
      "Configuration saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9600/config.json\n",
      "Model weights saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9600/pytorch_model.bin\n",
      "tokenizer config file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9600/tokenizer_config.json\n",
      "Special tokens file saved in /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-9600/special_tokens_map.json\n",
      "Deleting older checkpoint [/data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-600] due to args.save_total_limit\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "Loading best model from /data3/monty/deberta-v3-base-finetuned-test-run/checkpoint-6900 (score: 0.8266666666666668).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=9600, training_loss=0.0004251273045599646, metrics={'train_runtime': 7868.4968, 'train_samples_per_second': 19.407, 'train_steps_per_second': 1.22, 'total_flos': 4.006130538500283e+16, 'train_loss': 0.0004251273045599646, 'epoch': 100.0})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the test set don't have a corresponding argument in `DebertaV2ForTokenClassification.forward` and have been ignored: chunk_id, id. If chunk_id, id are not expected by `DebertaV2ForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Prediction *****\n",
      "  Num examples = 966\n",
      "  Batch size = 16\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='61' max='61' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [61/61 00:16]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "pred = trainer.predict(ner_dataset['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a3ef70bbdcd4730b2486673a073b8e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.chunking import compose_chunk_predictions_with_samples\n",
    "\n",
    "dev_dataset = compose_chunk_predictions_with_samples(dataset['dev'], pred, ner_dataset['test']['id'], tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['content', 'language', 'license', 'path', 'annotation_id', 'pii', 'id', 'fold', 'input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'labels', 'pred'],\n",
       "    num_rows: 100\n",
       "})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "true_labels = np.array(list(itertools.chain(*dev_dataset['labels'])))\n",
    "pred_labels = np.argmax(list(itertools.chain(*dev_dataset['pred'])), axis=-1)\n",
    "\n",
    "data = confusion_matrix(true_labels, pred_labels, labels=range(len(ID2LABEL)), normalize = 'true')\n",
    "df_cm = pd.DataFrame(data, columns=ID2LABEL, index = ID2LABEL)\n",
    "df_cm.index.name = 'Actual'\n",
    "df_cm.columns.name = 'Predicted'\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15, 15))\n",
    "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n",
    "\n",
    "sns.heatmap(df_cm, cbar=False, annot=True, cmap=cmap, square=True, fmt='.1%',\n",
    "            annot_kws={'size': 10})\n",
    "plt.title('Actuals vs Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /data3/monty/datasets/pii-for-code-folds/train/cache-0332dd98b41f0387.arrow\n"
     ]
    }
   ],
   "source": [
    "dev_dataset = compose_chunk_predictions_with_samples(dataset['dev'], pred, ner_dataset['test']['id'], tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.postprocessing import retokenize_with_logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The `retokenize_with_logits` function re-tokenizes the `content` by RegexpTokenizer and aggregates `pred` logits for tokens which had been merged.\n",
    "\n",
    "__Example:__\n",
    "\n",
    "Let for next string:\n",
    "\n",
    "    content = \"\\\n",
    "    # Created by Big Koddy McModel <bigkoddy@examplemail.com>\n",
    "    'SUPER_SECRET_KEY':'1234LjlkdslfKSLJ'\"\n",
    "\n",
    "we have next tokenization and logits:\n",
    "\n",
    "     #| Created| by| Big| Ko|ddy| Mc|Model| <|big|ko|ddy|@|example|mail|.|com|> ... | - tokens\n",
    "     [    0    , 0 , 0.9, .8, .9, 1., 0.97, 0, .8,.9,.9,.9, 0.89  , 1. ,1.,1.,0 ... ] - logits\n",
    "\n",
    "\n",
    " then `retokenize_with_logits` transforms it into next:\n",
    " \n",
    "     #| Created| by| Big| Koddy| McModel| <|bigkoddy|@|examplemail.com|>|'|SUPER_SECRET_KEY|'|:|'|1234LjlkdslfKSL|'\n",
    "     [      0 ,  0, 0.9,  0.95,    0.98, 0,   0.99, .9,   0.9        , 0,0,        0       ,0,0,0,       0.98     ]\n",
    "     \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "034f38f274e949bc93e27bea6ab5c002",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a48930e74f994b2898436548729185eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dev_dataset = dev_dataset.map(lambda x: retokenize_with_logits(**x)).map(label_tokenized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bd24a40e7b4417a98faec3fba6b13d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dev_dataset = dev_dataset.map(lambda x: dict(\n",
    "    pred_labels=[ID2LABEL[np.argmax(pred, axis=-1)] for pred in x['pred']],\n",
    "    true_labels=[ID2LABEL[label] for label in x['labels']]\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the latest cached version of the module from /home/monty/.cache/huggingface/modules/datasets_modules/metrics/seqeval/c8563af43bdce095d0f9e8b8b79c9c96d5ea5499b3bf66f90301c9cb82910f11 (last modified on Thu Feb 16 17:58:29 2023) since it couldn't be found locally at seqeval, or remotely on the Hugging Face Hub.\n",
      "/data1/monty/miniconda3/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_metric\n",
    "seqeval_metric = load_metric(\"seqeval\")\n",
    "ner_metrics = seqeval_metric.compute(predictions=dev_dataset['pred_labels'], references=dev_dataset['true_labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_745f0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_745f0_level0_col0\" class=\"col_heading level0 col0\" >precision</th>\n",
       "      <th id=\"T_745f0_level0_col1\" class=\"col_heading level0 col1\" >recall</th>\n",
       "      <th id=\"T_745f0_level0_col2\" class=\"col_heading level0 col2\" >f1</th>\n",
       "      <th id=\"T_745f0_level0_col3\" class=\"col_heading level0 col3\" >number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_745f0_level0_row0\" class=\"row_heading level0 row0\" >AMBIGUOUS</th>\n",
       "      <td id=\"T_745f0_row0_col0\" class=\"data row0 col0\" >0.0%</td>\n",
       "      <td id=\"T_745f0_row0_col1\" class=\"data row0 col1\" >0.0%</td>\n",
       "      <td id=\"T_745f0_row0_col2\" class=\"data row0 col2\" >0.0%</td>\n",
       "      <td id=\"T_745f0_row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_745f0_level0_row1\" class=\"row_heading level0 row1\" >EMAIL</th>\n",
       "      <td id=\"T_745f0_row1_col0\" class=\"data row1 col0\" >96.1%</td>\n",
       "      <td id=\"T_745f0_row1_col1\" class=\"data row1 col1\" >96.1%</td>\n",
       "      <td id=\"T_745f0_row1_col2\" class=\"data row1 col2\" >96.1%</td>\n",
       "      <td id=\"T_745f0_row1_col3\" class=\"data row1 col3\" >51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_745f0_level0_row2\" class=\"row_heading level0 row2\" >IP_ADDRESS</th>\n",
       "      <td id=\"T_745f0_row2_col0\" class=\"data row2 col0\" >85.7%</td>\n",
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       "      <td id=\"T_745f0_row2_col3\" class=\"data row2 col3\" >18</td>\n",
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       "      <td id=\"T_745f0_row3_col0\" class=\"data row3 col0\" >42.9%</td>\n",
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       "    <tr>\n",
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       "      <td id=\"T_745f0_row4_col1\" class=\"data row4 col1\" >87.0%</td>\n",
       "      <td id=\"T_745f0_row4_col2\" class=\"data row4 col2\" >87.9%</td>\n",
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       "      <td id=\"T_745f0_row5_col2\" class=\"data row5 col2\" >80.0%</td>\n",
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